Ryan Yudha Adhitya, Noorman Rinanto, Rahardhita Widyatra Sudibyo, Sapto Wibowo, Nuryanti, Fendik Eko Purnomo, Muhammad Rizani Rusli, Sarosa Castrena Abadi, Chandra Wiharya, Faisal Lutfi Afriansyah, Anif Jamaluddin, Nurul Zainal Fanani
Battery management systems are essential in electric vehicles and renewable energy applications, especially in terms of ensuring optimal battery health and performance and regarding the state of charge (SOC) in batteries consisting of many cells. The lifetime and efficiency of the battery depend on the accuracy of the SOC parameter estimation. Moreover, systems that apply active balancing technology are able to move cells with high SOC data to cells with low SOC. Many methods have been developed, but their long execution time makes them less optimal when applied. High-speed SOC estimation is required in active balancing technology, in addition to high accuracy. Therefore, this study proposes the estimation of SOC parameters using a statistical and metaheuristic approach from voltage and current input data in each battery cell. The experimental results showed that the metaheuristic-based method (ANFIS) had better RSME and R2 values compared with the polynomial and linear regression or even the machine learning-based method (recurrent neural network) for training data. © 2026 by the authors.
Department of Marine Electrical Engineering, Politeknik Perkapalan Negeri Surabaya, Surabaya, 60111, Indonesia; Department of Electrical Engineering, Politeknik Elektronika Negeri Surabaya, Surabaya, 60111, Indonesia; Department of Electrical Engineering, Politeknik Negeri Malang, Malang, 65141, Indonesia; Department of Manufacturing Automation and Mechatronics Engineering, Politeknik Manufaktur Bandung, Bandung, 40135, Indonesia; Department of Engineering, Politeknik Negeri Jember, Jember, 68101, Indonesia; Department of Information Technology, Politeknik Negeri Jember, Jember, 68101, Indonesia; Department of Physics Education, Universitas Sebelas Maret, Surakarta, 57126, Indonesia